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Project Proposal (PDF) - Oxford Brookes University

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FP7-ICT-2011-9 STREP proposal<br />

18/01/12 v1 [Dynact]<br />

Annex I: Rerefences<br />

[1] S. Ali, A. Basharat, and M. Shah, Chaotic invariants for human action recognition, ICCV’07.<br />

[2] S.-I. Amari, Differential geometric methods in statistics, Springer-Verlag, 1985.<br />

[3] A. Bar-Hillel, T. Hertz, N. Shental, and D.Weinshall, Learning distance functions using equivalence<br />

relations, ICML03, 2003, pp. 11–18.<br />

[4] M. Bilenko, S. Basu, and R. J. Mooney, Integrating constraints and metric learning in semi-supervised<br />

clustering, Proc. of ICML’04, 2004.<br />

[5] A. Bissacco, A. Chiuso, and S. Soatto, Classification and recognition of dynamical models: The role of<br />

phase, independent components, kernels and optimal transport, IEEE Trans. PAMI 29 (2007), no. 11, 1958–<br />

1972.<br />

[6] M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, Actions as space-time shapes, IEEE<br />

Conference on Computer Vision (2005), 1395–1402.<br />

[7] M. Bregonzio, S. Gong, and T. Xiang, Recognising action as clouds of space-time interest points,<br />

CVPR’09, pp. 1948–1955.<br />

[8] P. Burman, A comparative study of ordinary crossvalidation, v-fold cross-validation and the repeated<br />

learning-testing methods, Biometrika 76(3) (1989), 503–514.<br />

[9] N. L. Carter, D. Young, and J. M. Ferryman, Supplementing Markov chains with additional features for<br />

behavioural analysis, 2006, pp. 65–65.<br />

[10] R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal, Histograms of oriented optical flow and Binet-<br />

Cauchy kernels on nonlinear dynamical systems for the recognition of human actions, CVPR’09, pp. 1932–<br />

1939.<br />

[11] F. Cuzzolin, Using bilinear models for view-invariant action and identity recognition, CVPR’06, vol. 1,<br />

pp. 1701–1708.<br />

[12] F. Cuzzolin, A geometric approach to the theory of evidence, IEEE Transactions on Systems, Man, and<br />

Cybernetics – Part C 38 (2008), no. 4, 522–534.<br />

[13] F. Cuzzolin, Learning pullback metrics for linear models, Workshop on Machine Learning for Visionbased<br />

Motion Analysis MLVMA, 2008.<br />

[14] F. Cuzzolin, Multilinear modeling for robust identity recognition from gait, Behavioral Biometrics for<br />

Human Identification: Intelligent Applications (Liang Wang and Xin Geng, eds.), IGI, 2009.<br />

[15] F. Cuzzolin, Three alternative combinatorial formulations of the theory of evidence, Intelligent Decision<br />

Analysis (2010).<br />

[16] F. Cuzzolin, Manifold learning for multi-dimensional autoregressive dynamical models, Machine<br />

Learning for Visionbased Motion Analysis (L. Wang, G. Zhao, L. Cheng, and M. Pietikine, eds.), Springer,<br />

2010.<br />

[17] F. Cuzzolin, D. Mateus, D. Knossow, E. Boyer, and R. Horaud, Coherent Laplacian protrusion<br />

segmentation, CVPR’08.<br />

[18] F. Cuzzolin, A. Sarti and S. Tubaro, Action modeling with volumetric data, ICIP’04, vol. 2, pp. 881–<br />

884.<br />

[19] N. Dalai and B. Triggs, Histograms of oriented gradients for human detection, CVPR’06, pp. 886– 893.<br />

[20] M. N. Do, Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov<br />

models, IEEE Signal Processing Letters 10 (2003), no. 4, 115 – 118.<br />

[21] O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce, Automatic annotation of human actions in<br />

video, ICCV’09.<br />

[22] C. F. Eick, A. Rouhana, A. Bagherjeiran, and R. Vilalta, Using clustering to learn distance functions for<br />

supervised similarity assessment, ICML and Data Mining, 2005.<br />

[23] R. Elliot, L. Aggoun, and J. Moore, Hidden markov models: estimation and control, Springer Verlag,<br />

1995.<br />

[24] J. M. Wang et. al., Gaussian process dynamical model, NIPS’05.<br />

[25] L. Ralaivola et al, Dynamical modeling with kernels for nonlinear time series prediction, NIPS’04.<br />

[26] A. Galata, N. Johnson, and D. Hogg, Learning variable length Markov models of behavior, CVIU 81<br />

(2001), no. 3, 398–413.<br />

[27] A. Gilbert, J. Illingworth and R. Bowden, Fast realistic multi-action recognition using mined dense<br />

spatio-temporal features, Proc. of ICCV, pp. 925-931, 2009.<br />

[28] A. Gupta, P. Srinivasan, J. Shi, and L. S. Davis, Understanding videos, constructing plots learning a<br />

visually grounded storyline model from annotated videos., CVPR’09, pp. 2012–2019.<br />

[29] D. Han, L. Bo, and C. Sminchisescu, Selection and context for action recognition, ICCV’09.<br />

<strong>Proposal</strong> Part B: page [63] of [67]

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